Visually meaningful encryption for color images by using Qi hyper-chaotic system and singular value decomposition in YCbCr color space

Optik ◽  
2020 ◽  
Vol 213 ◽  
pp. 164422 ◽  
Author(s):  
Yu-Guang Yang ◽  
Lu Zou ◽  
Yi-Hua Zhou ◽  
Wei-Min Shi
Keyword(s):  
Singular Value Decomposition ◽  
Chaotic System ◽  
Color Space ◽  
Singular Value ◽  
Color Images ◽  
Ycbcr Color Space ◽  
Value Decomposition

scholarly journals Robust Video Watermarking using Tchebichef Transform and Singular Value Decomposition on the Selected Frame Based YCbCr Color Space

2020 ◽  
Vol 13 (6) ◽  
pp. 432-441
Author(s):  
Andik Setyono ◽  
◽  
De Setiadi ◽  
Keyword(s):  
Singular Value Decomposition ◽  
Color Space ◽  
Singular Value ◽  
Video Watermarking ◽  
Average Value ◽  
Ycbcr Color Space ◽  
Watermark Embedding ◽  
Value Decomposition ◽  
Linear Congruential ◽  
Measuring Tool

Watermarking is a copyright authentication technique. This research proposes a robust watermarking method with a combination of Tchebichef transformation and singular value decomposition (SVD). To maintain imperceptibility, embedding is done on one of the selected frames. Frames are randomly selected to increase watermark security. Frame selection is based on two integer keys processed by a linear congruential generator (LCG). The selected frame is then converted to its color space from RGB to YCbCr. Y channel (luminance) was selected to be processed by Tchebichef transformation based on block 8 × 8, the coefficient 0.0 for each block of the transformed results was selected and collected on a matrix. This matrix is then transformed with SVD and a singular matrix is selected for watermark embedding, this method is done to increase robustness. Based on the test results, the imperceptibility value is very good with an average value of 50.952dB, based on the PSNR as a measuring tool. Whereas in the robustness aspect, a value of 0.927 is generated based on the results of the measurement of the correlation between the watermark and the original watermark, where these results are the average extraction results without and with various attacks.

scholarly journals Objects Detection by Singular Value Decomposition Technique in Hybrid Color Space: Application to Football Images

2010 ◽  
Vol 5 (2) ◽  
pp. 193
Author(s):  
Mourad Moussa Jlassi ◽  
Ali Douik ◽  
Hassani Messaoud
Keyword(s):  
Singular Value Decomposition ◽  
Color Space ◽  
Singular Value ◽  
Space Application ◽  
Color Information ◽  
Novel Approach ◽  
Computer Vision Applications ◽  
Value Decomposition ◽  
Objects Detection ◽  
Background Pixel

In this paper, we present an improvement non-parametric background modeling and foreground segmentation. This method is important; it gives the hand to check many states kept by each background pixel. In other words, generates the historic for each pixel, indeed on certain computer vision applications the background can be dynamic; several intensities were projected on the same pixel. This paper describe a novel approach which integrate both Singular Value Decomposition (SVD) of each image to increase the compactness density distribution and hybrid color space suitable to this case constituted by the three relevant chromatics levels deduced by histogram analysis. In fact the proposed technique presents the efficiency of SVD and color information to subtract background pixels corresponding to shadows pixels. This method has been applied on colour images issued from soccer video. In the other hand to achieve some statistics information about players ongoing of the match (football, handball, volley ball, Rugby...) as well as to refine their strategy coach and leaders need to have a maximum of technical-tactics information. For this reason it is prominent to elaborate an algorithm detecting automatically interests color regions (players) and solve the confusion problem between background and foreground every moment from images sequence.

scholarly journals Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Vahid Faghih Dinevari ◽  
Ghader Karimian Khosroshahi ◽  
Mina Zolfy Lighvan
Keyword(s):  
Singular Value Decomposition ◽  
Capsule Endoscopy ◽  
Color Space ◽  
Wireless Capsule Endoscopy ◽  
Singular Value ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Data Set ◽  
Value Decomposition ◽  
Wireless Capsule

Wireless capsule endoscopy (WCE) is a new noninvasive instrument which allows direct observation of the gastrointestinal tract to diagnose its relative diseases. Because of the large number of images obtained from the capsule endoscopy per patient, doctors need too much time to investigate all of them. So, it would be worthwhile to design a system for detecting diseases automatically. In this paper, a new method is presented for automatic detection of tumors in the WCE images. This method will utilize the advantages of the discrete wavelet transform (DWT) and singular value decomposition (SVD) algorithms to extract features from different color channels of the WCE images. Therefore, the extracted features are invariant to rotation and can describe multiresolution characteristics of the WCE images. In order to classify the WCE images, the support vector machine (SVM) method is applied to a data set which includes 400 normal and 400 tumor WCE images. The experimental results show proper performance of the proposed algorithm for detection and isolation of the tumor images which, in the best way, shows 94%, 93%, and 93.5% of sensitivity, specificity, and accuracy in the RGB color space, respectively.

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